研究方向：数据挖掘、复杂系统与复杂网络、电子商务、人工智能

学术论文

Ju Xiang, Yan Zhang, Jian-Ming Li, Hui-Jia Li, and Min Li.Journal of Statistical Mechanics: Theory and Experiment:2019,2019,033403摘要Optimizing statistical measures for community structure is one of the most popular strategies for community detection, but many of them lack the flexibility of resolution and thus are incompatible with multi-scale communities of networks. Here, we further studied a statistical measure of interest for community detection, asymptotic surprise which is asymptotic approximation of surprise. We analyzed the critical behaviors of asymptotic surprise in the phase transition of community partition theoretically. Then, according to the theoretical analysis, a multi-resolution method based on asymptotic surprise was introduced, which provides an alternative approach to study multi-scale communities in networks, and an improved Louvain algorithm was proposed to optimize the asymptotic surprise more effectively. By a series of experimental tests in various networks, we further demonstrated the critical behaviors of the asymptotic surprise, and the effectiveness of the improved Louvain algorithm; and then we validated the ability of our multi-resolution method to solve the first-type resolution limit and its strong tolerance against the second-type resolution limit; finally we confirmed its effectiveness in revealing multi-scale community structures in networks.

Zhan Bu, Hui-Jia Li, Zhen Wang, Guangliang Gao, Jie Cao.IEEE Transactions on Cybernetics:2019,49(1),328-341摘要Besides the topological structure, there are additional information, i.e., node attributes, on top of the plain graphs. Usually, these systems can be well modeled by attributed graphs, where nodes represent component actors, a set of attributes describe users portraits and edges indicate their connections. An elusive question associated with attributed graphs is to study how clusters with common internal properties form and evolve in real-world networked systems with great individual diversity, which leads to the so-called problem of attributed graph clustering (AGC). In this paper, we comprehended AGC naturally as a dynamic cluster formation game (DCFG), where each nodes feasible action set can be constrained by every cluster in a discrete-time dynamical system. Specifically, we carried out a deep research on a special case of finite dynamic games, named dynamic social game (DSG), the convergence of the finite Nash equilibrium sequence in a DSG was also proved strictly. By carefully defining the feasible action set and the utility function associated with each node, the proposed DCFG can be well related to a DSG; and we showed that a balanced solution of AGC could be found by solving a finite set of coupled static Nash equilibrium problems in the related DCFG. We, finally, proposed a self-learning algorithm, which can start from any arbitrary initial cluster configuration, and, finally, find the corresponding balanced solution of AGC, where all nodes and clusters are satisfied with the final cluster configuration. Extensive experiments were applied on real-world social networks to demonstrate both effectiveness and scalability of the proposed approach by comparing with the state-of-the-art graph clustering methods in the literature.

Hui-Jia Li, Lin Wang.New Journal of Physics:2019,21,015005摘要The studies of multiplex networks are increasingly popular in recent years. Modeling multiple complex systems as a multiplex network has refreshed our understanding about the structure and dynamics of various real-world systems. As an important variant of the voter models, belief formation dynamics such as the asynchronous belief percolation (ABP) model has attracted much attention from statistical physics and network science communities. Existing studies of the ABP model mainly focus on the applications to single networks, whereas how the structure of multiplex networks affects its dynamical behavior is still not well understood. To close this gap, we propose a multi-scale ABP model that takes into account the differential velocities of belief propagation at different subnetworks within the multiplex network. Using extensive computer simulations, we find that (i) increasing the degree correlation between subnetworks can promote nodes with minority belief to form stable clusters and (ii) minority nodes require less initial supports to survive in multiplex networks with respect to single networks. Our conclusion is robust against the detailed topology of the subnetworks that constitute the multiplex network.

Sheng Wei, Shuqing N. Teng, Hui-Jia Li, et al.Plos One:2019,14(2),e0211052摘要Presently, China has the largest high-speed rail (HSR) system in the world. However, our understanding of the network structure of the world’s largest HSR system remains largely incomplete due to the limited data available. In this study, a publicly available data source, namely, information from a ticketing website, was used to collect an exhaustive dataset on the stations and routes within the Chinese HSR system. The dataset included all 704 HSR stations that had been built as of June, 2016. A classical set of frequently used metrics based on complex network theory were analyzed, including degree centrality, betweenness centrality, and closeness centrality. The frequency distributions of all three metrics demonstrated highly consistent bimodal-like patterns, suggesting that the Chinese HSR network consists of two distinct regimes. The results indicate that the Chinese HSR system has a hierarchical structure, rather than a scale-free structure as has been commonly observed. To the best of our knowledge, such a network structure has not been found in other railway systems, or in transportation systems in general. Follow-up studies are needed to reveal the formation mechanisms of this hierarchical network structure.

Xuelong Li, Marko Jusup, Zhen Wang, Hui-Jia Li, Lei Shi, Boris Podobnik, H. Eugene Stanley, Shlomo Havlin, Stefano Boccaletti.Proceedings of the National Academy of Sciences of the United States of America:2018,115(1),30–35摘要Network reciprocity has been widely advertised in theoretical studies as one of the basic cooperation-promoting mechanisms, but experimental evidence favoring this type of reciprocity was published only recently. When organized in an unchanging network of social contacts, human subjects cooperate provided the following strict condition is satisfied: The benefit of cooperation must outweigh the total cost of cooperating with all neighbors. In an attempt to relax this condition, we perform social dilemma experiments wherein network reciprocity is aided with another theoretically hypothesized cooperation-promoting mechanism—costly punishment. The results reveal how networks promote and stabilize cooperation. This stabilizing effect is stronger in a smaller-size neighborhood, as expected from theory and experiments. Contrary to expectations, punishment diminishes the benefits of network reciprocity by lowering assortment, payoff per round, and award for cooperative behavior. This diminishing effect is stronger in a larger-size neighborhood. An immediate implication is that the psychological effects of enduring punishment override the rational response anticipated in quantitative models of cooperation in networks.

Ju Xiang, Hui-Jia Li, Zhan Bu, Zhen Wang, et al..Scientific Reports:2018,8,14459摘要Module or community structures widely exist in complex networks, and optimizing statistical measures is one of the most popular approaches for revealing and identifying such structures in real-world applications. In this paper, we focus on critical behaviors of (Quasi-)Surprise, a type of statistical measure of interest for community structure, accompanied by a series of comparisons with other measures. Specially, the effect of various network parameters on the measures is thoroughly investigated. The critical number of dense subgraphs in partition transition is derived, and a kind of phase diagrams is provided to display and compare the phase transitions of the measures. The effect of “potential well” for (Quasi-)Surprise is revealed, which may be difficult to get across by general greedy (agglomerative or divisive) algorithms. Finally, an extension of Quasi-Surprise is introduced for the study of multi-scale structures. Experimental results are of help for understanding the critical behaviors of (Quasi-)Surprise, and may provide useful insight for the design of effective tools for community detection.

Ke Hu, Jing-Bo Hu, Liang Tang, Ju Xiang, Jin-Long Ma, Yuan-Yuan Gao, Hui-Jia Li, Yan Zhang.Journal of Statistical Mechanics: Theory and Experiment:2018,2018,100001摘要Network-based computational approaches in the prediction of genes that are associated with diseases are of considerable importance in uncovering the molecular basis of human diseases. Here, we proposed a novel disease-gene-prediction method by combining path-based structure with community structure characteristics in human protein–protein networks. A new similarity measure was first proposed that is based on the path and community structures of networks and leverages community structures for disease-gene prediction. Then, the distinguishing capacity of the methods to identify disease genes from non-disease genes was assessed statistically to analyze their ability to predict disease genes. Finally, the new method was applied to disease-gene prediction for several datasets, and the performances of the measures in disease-gene prediction were analyzed, with an emphasis on assessing the effect of community structure on the predictive performance. The results indicated an ability of the new method to predict disease-genes in several networks and within several disease classes. Further, the results reported here confirm that the incorporation of community structures can indeed improve the performance of disease-gene prediction methods.

Hui-Jia Li, Zhan Bu, Zhen Wang, Jie Cao, Yong Shi.IEEE Transactions on Emerging Topics in Computational Intelligence:2018,2(3),214-223摘要Networked systems with high computational efficiency are desired in many applications ranging from sociology to engineering. Generally, the performance of the network computation can be enhanced by two ways: rewiring and weighting. In this paper, we proposed a new two-modes weighting strategy based on the concept of communication neighbor graph, which takes use of both the local and global topological properties, e.g., degree centrality, betweenness centrality, and closeness centrality. The weighting strategy includes two modes: In the original mode, it enhances the network synchronizability by increasing the weights of bridge edges; whereas in the inverse version, it increases the significance of community structure by decreasing the weights of bridge edges. The scheme of weighting is controlled by only one parameter, i.e., α, which can be easily performed. We test the effectiveness of our model on a number of artificial benchmark networks as well as real-world ones. To the best of our knowledge, the proposed weighting strategy can outperform the existing methods in improving the performance of network computation.

Shi Chen, Zhi-Zhong Wang, Liang Tang, Yan-Ni Tang, Yuan-Yuan Gao, Hui-Jia Li, Ju Xiang and Yan Zhang.Plos One:2018,13(10),e0205284摘要Community structures are ubiquitous in various complex networks, implying that the networks commonly be composed of groups of nodes with more internal links and less external links. As an important topic in network theory, community detection is of importance for understanding the structure and function of the networks. Optimizing statistical measures for community structures is one of most popular strategies for community detection in complex networks. In the paper, by using a type of self-loop rescaling strategy, we introduced a set of global modularity functions and a set of local modularity functions for community detection in networks, which are optimized by a kind of the self-consistent method. We carefully compared and analyzed the behaviors of the modularity-based methods in community detection, and confirmed the superiority of the local modularity for detecting community structures on large-size and heterogeneous networks. The local modularity can more quickly eliminate the first-type limit of modularity, and can eliminate or alleviate the second-type limit of modularity in networks, because of the use of the local information in networks. Moreover, we tested the methods in real networks. Finally, we expect the research can provide useful insight into the problem of community detection in complex networks.

Hui-Jia Li, Zhan Bu, Yulong Li, Zhongyuan Zhang, et al..Chaos, Solitons & Fractals:2018,110,20-27摘要In real networks, clustering is of great value to the analysis, design, and optimization of numerous complex systems in natural science and engineering, e.g. power supply systems ,modern transportation networks, and real-world networks. However, the majority of them simply pay attention to the density of edges rather than the signs of edges as the attributes to cluster, which usually suffer a high-level computational complexity. In this paper, a new rule is proposed to update the attributes flow, which can guarantee network clustering reach a state of optimal convergence. The positive and negative update rule we introduced, represent the cooperative and hostile relationship, and the attribute configuration will convergence and one can identify the reasonable cluster configuration automatically. An algorithm with high efficiency is proposed: a nearly linear relationship is found between the time complexity and the size in sparse networks. Finally, we conduct the verification of the algorithmic performance by a representative simulations on Correlates of War data.

Hui-Jia Li, Junhua Zhang, Zhi-Ping Liu, Luonan Chen, Xiang-Sun Zhang.European Physical Journal B:2012,85(6),109摘要Most existing approaches for community detection require complete information of the graph in a specific scale, which is impractical for many social networks. We propose a novel algorithm that does not embrace the universal approach but instead of trying to focus on local social ties and modeling multi-scales of social interactions occurring in those networks. Our method for the first time optimizes the topological entropy of a network and uncovers communities through a novel dynamic system converging to a local minimum by simply updating the membership vector with very low computational complexity. It naturally supports overlapping communities through associating each node with a membership vector which describes node’s involvement in each community. Furthermore, different multi-scale partitions can be obtained by tuning the characteristic size of modules from the optimal partition. Because of the high efficiency and accuracy of the algorithm, it is feasible to be used for the accurate detection of community structures in real networks.

Yinghong Ma, Hui-Jia Li, Xiaodong Zhang.Journal of Statistical Mechanics: Theory and Experiment:2010,11,11009摘要Since some realistic networks are influenced not only by increment behavior but also by the tunable clustering mechanism with new nodes to be added to networks, it is interesting to characterize the model for those actual networks. In this paper, a weighted local-world model, which incorporates increment behavior and the tunable clustering mechanism, is proposed and its properties are investigated, such as degree distribution and clustering coefficient. Numerical simulations are fitted to the model and also display good right-skewed scale-free properties. Furthermore, the correlation of vertices in our model is studied which shows the assortative property. The epidemic spreading process by weighted transmission rate on the model shows that the tunable clustering behavior has a great impact on the epidemic dynamic.